Leveraging Artificial Intelligence and Machine Learning for Market Prediction in the Fintech Industry: A Comparative Analysis of Predictive Models and Their Impact on Financial Decision-Making

Abstract

The fintech industry benefits from Artificial Intelligence (AI) and Machine Learning (ML) which provide superior market prediction abilities that exceed traditional forecasting techniques. The extensive utilization of financial data allows these technologies to uncover patterns and achieve better predictions for enhanced data-driven choices. The precision of market predictions serves as an essential factor in financial operations because it determines how investment choices and risk adjustments along with market equilibrium are carried out. The financial industry relies on two main forecasting models based on time series analysis (ARIMA, GARCH) and regression methods for their predictions. AI-driven models harness supervised learning through Random Forest, Support Vector Machines, Neural Networks while utilizing unsupervised learning with clustering for anomaly detection and deep learning through Recurrent Neural Networks, Long Short-Term Memory to achieve better performance in complex market analysis. FinTech predictive analytics now benefits from advanced predictive tools based on reinforcement learning alongside hybrid methods that integrate traditional techniques with AI resources. The paper features an assessment of market prediction capabilities between the introduced predictive models. Predictive accuracy is assessed through multiple performance metrics which include Root Mean Square Error (RMSE), Mean Absolute Error (MAE) together with Mean Absolute Percentage Error (MAPE). The paper evaluates how AI-forecasting influences financial decision processes while analyzing its benefits for risk management alongside regulatory and ethical aspects of utilizing AI for predictions. The authors evaluate real-world financial market prediction implementations through both successful and unsuccessful AI examples. The study reveals that AI delivers enhanced forecasting precision along with improved decision support yet data prejudice and algorithm clarity along with regulatory standards continue to present substantial challenges. Future developments in quantum computing techniques alongside behavioral finance alignment and federated learning will redefine AI-driven market prediction capabilities.